Neuroimaging data and file structures in Python - Exercise
Description:
This exercise is a copy from Brainkhack School, but have small adaptation and change the recipient.
Instructions:
Follow the steps on Brainhack School to install required package and download the data and notebook files. Then open the notebook files with jupyter notebook.
Watch the video and read through the Nibabel.py. You only to learn the basic contents to complete the excercise, some advanced contents (from 1:14:17 to 1:34:38) are optional and you can skip.
After the tutorial, you should open a separate notebook and complete the following exercises:
Use the nilearn library function fetch_atlas_difumo to get the 64 parcellation image. As we learned in the video, the data in this image is just a number array and you can maniputate it with numpy. Please extract the 16th region, binarize it, and save it as a new nifti image.
Use the slicer object to view the new nifti file we created in the three different views.
Bonus: in 200 words, describe the conceptual differences between array and array proxy images.
Submission:
You should have one notebook file containing the code for the exercise, or you can write your code on colab.
Please send an email to brainhackschooltaiwan@gmail.com with the subject title [BHSTW] <Your_Student_ID> Neuroimaging data and file structures in Python (e.g., [BHSTW] B05202021 Neuroimaging data and file structures in Python), and include the associated notebook file or colab link.